Targeting videos based on viewer similarity

ABSTRACT

Presentation of a video clip is made to persons having a high probability of viewing the clip. A database containing viewers of previously offered video clips is analyzed to determine similarities of preferences among viewers. When a new video clip has been watched by one or more viewers in the database, those viewers who have watched the new clip with positive results are compared with others in the database who have not yet seen it. Prospective viewers with similar preferences are identified as high likelihood candidates to watch the new clip when presented. Bids to offer the clip are based on the degree of likelihood. For one embodiment, a data collection agent (DCA) is loaded to a player and/or to a web page to collect viewing and behavior information to determine viewer preferences. Viewer behavior may be monitored passively by different disclosed methods.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/348,780, filed Nov. 10, 2016, which is continuation of U.S. patentapplication Ser. No. 13/111,705, filed on May 19, 2011, now issued asU.S. Pat. No. 9,612,995, which is a continuation-in-part of U.S. patentapplication Ser. No. 12/904,552, filed Oct. 14, 2010, now issued as U.S.Pat. No. 8,549,550, which is a continuation-in-part of U.S. patentapplication Ser. No. 12/212,556, filed Sep. 17, 2008, now issued as U.S.Pat. No. 8,577,996. The aforementioned applications are herebyincorporated by reference in their entirety.

BACKGROUND

Technical Field

The invention relates to the monitoring of a website and the interactionof a client with a website relative to web content. More particularly,the invention relates to passively monitoring online video viewingbehavior to determine viewer preference similarities, and to determinethe likelihood that a viewer chooses to view a particular video clipwhen offered.

Description of the Background Art

Video clips may be supplied to viewers from any website for information,entertainment, or advertising purposes. Some of these websites may beWeb 2.0 websites where a user can create an account, upload, share,contribute, comment, vote, or read personal opinions of other users, allon the same site. When video clips are viewed purely for entertainmentpurposes, users may be more motivated to rate a particular video clipaccording to their preferences. However, not all viewers expend theeffort to rate a video, even if they feel strongly about it.

Video sharing and online video services allow individuals or contentpublishers to upload video clips to Internet websites. The websitestores the video clip on its server, and provides different types offunctions to allow others to view that video clip. These websites mayallow commenting and rating of a video clip. Many services have optionsfor private sharing and other publication options. Video sharingservices can be classified into several categories including, usergenerated video sharing websites, video sharing platform, white labelproviders, and web based video editing.

As video hosting websites become increasingly popular, such websitesprovide a platform for traditional publishers, such as televisionbroadcasters, to use these websites as another medium to display mediacontent which may be of a more informative nature. For example, CBS andCNN networks often publish video clips on YouTube. For such publishers,it is highly desirable to know the ratings of their published videoclips. In television the ratings, e.g. Nielsen Ratings, estimate theaudience size and composition of television programming, and thusdetermine advertising rates. This method is not applicable for theInternet. Again, given the opportunity to rate a particular video clip,not all viewers expend the effort to rate the video clip, even if theyfeel strongly about it.

Either independent of, or associated with, entertainment or informativevideo clips, advertisement video clips may also be supplied to onlineusers. Websites that supply such advertisement video clips may or maynot provide users a means to rate such clips. In circumstances where theadvertisement is embedded as part of an entertainment or informativeclip, such as a pre-roll advertisement, offering users a voluntaryrating facility for the advertisement portion becomes difficult from apracticality standpoint.

In the related art there are different techniques to determine thepopularity of a website. One technique known in the art refers topage-hit or page views. The page-hit refers to an event in which aserver receives a request for a page and then serves up the page. Acommon measure of traffic at a website is the number of page hits,especially in. an advertising context, for particular pages or sets ofpages. Page-hit counts are a rough measure of the traffic of a website.Other techniques involve the analyzing of the traffic between a Webserver and clients. Such prior art techniques work well when the trafficof interest relates to particular pages, but are generally notinformative when traffic by topic is desired because one page may relateto multiple topics. Systems have been suggested for embedding scriptcode in web pages for tracking user activity on a web page.

Another technique for determining the rating of video clips published ononline video sites is based on viewership information provided by thesesites. Typically, the sites count the cumulative number of users whoview the clip. However, more refined measurements that include, forexample, the quantity and characteristics of viewers, as well asdetailed information about the duration and repetition of each view, areneither generated by video sharing websites nor by any other prior arttechnique. Furthermore, viewership information is easily manipulated bythe use of, for example, scripts, browser refreshes, and other meansthat skew the results. As a result, ratings measurements that are basedon the viewership information are inaccurate at best and oftenmisleading.

Systems have been suggested for placement of advertising slots within orin proximity to hosted video clips. In such systems, methods are used totrack the ad placement and viewing. Such methods require preparation ofthe video clips for ad placement.

It would be therefore advantageous to provide a solution for onlinevideo analytics for generally available video clips on the Internet.

To acquire user preference information for a particular advertisementvideo clip effectively, and to expand the base of user preferenceinformation for any video clip beyond those viewers who voluntarily ratea video clip, it would be useful to provide a solution for acquiringviewership information reflecting viewer preferences without requiringviewers to provide their preferences proactively. Also, when aparticular video clip is in the form of an advertisement, it would beadvantageous to determine the best target viewers to whom the video maybe offered, especially if a bidding process is used to determine whichadvertising video clip is to be shown to a prospective viewer at aparticular moment in time.

SUMMARY OF THE INVENTION

An embodiment of the invention enables presentation of a video clip to apotential viewer who has a high probability of viewing the clip. Adatabase containing viewers of previously offered video clips isanalyzed to determine similarities of preferences among viewers. When anew video clip has been watched by one or more viewers in the database,those viewers who have watched the new clip with positive results arecompared with others in the database who have not yet seen it.Prospective viewers with similar preferences are identified as highlikelihood candidates to watch the new clip when presented. Bids tooffer the clip are based on the degree of likelihood. For oneembodiment, a data collection agent (DCA) is loaded to a player and/orto a web page to collect viewing and behavior information to determineviewer preferences. Viewer behavior may be monitored passively bydifferent disclosed methods.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a network used to describe the variousembodiments disclosed in accordance with the invention;

FIG. 2 is a block diagram of the VAS disclosed in accordance with anembodiment of the invention;

FIGS. 3A-3C show exemplary charts generated by the VAS according to oneembodiment of the invention;

FIG. 4 is a flowchart that describes the operation of the DCA inaccordance with an embodiment of the invention;

FIG. 5 is a flowchart for capturing, processing, creating and recordinga preference score for a video clip in accordance with an embodiment ofthe invention;

FIG. 6 is a block diagram showing possible communication alternativesfor sharing a video clip with other persons in accordance with anembodiment of the invention;

FIG. 7 shows a table containing elements of viewing duration analysisfor a sample of video clips and viewers in accordance with an embodimentof the invention;

FIG. 8 is a block diagram illustrating how viewers may be groupedaccording to videos viewed and similarity of preferences in an exampleanalysis according to the invention; and

FIG. 9 is a flow chart showing how preference similarities aredetermined and analyzed to determine the likelihood that a viewerchooses to watch a particular video when it is presented in accordancewith an embodiment of the invention.

DETAILED DESCRIPTION OF THE INVENTION

An embodiment of the invention enables presentation of a video clip to apotential viewer who has a high probability of viewing the clip. Adatabase containing viewers of previously offered video clips isanalyzed to determine similarities of preferences among viewers. When anew video clip has been watched by one or more viewers in the database,those viewers who have watched the new clip with positive results arecompared with others in the database who have not yet seen it.Prospective viewers with similar preferences are identified as highlikelihood candidates to watch the new clip when presented. Bids tooffer the clip are based on the degree of likelihood. For oneembodiment, a data collection agent (DCA) is loaded to a player and/orto a web page to collect viewing and behavior information to determineviewer preferences. Viewer behavior may be monitored passively bydifferent disclosed methods.

According to an embodiment of the invention, a profile is created ofthose viewers in a database of viewers who are deemed likely to watch aparticular video clip when presented. This profile is based on thecalculated likelihood for each viewer being over a specific likelihoodlevel defined for the profile. Subsequently, when an opportunity tooffer a video clip to a prospective viewer arises, a decision to offerthe video may include that prospective viewer being on a list of viewersthat matches the profile.

When video clips are used as advertisements, it is especially useful tobe able to predict the degree of likelihood that a particularprospective viewer chooses to watch a video clip not seen before by theviewer. If the advertiser must bid for the opportunity to offer a videoclip to a prospective viewer, the likelihood prediction is a criticalcomponent in computing the amount to bid, and the decision of how muchto bid for presenting the video can vary from viewer to viewer based ona specific likelihood calculation for the particular viewer.

According to an embodiment of the invention, determining the likelihoodthat a prospective viewer chooses to view a particular video clip isaccomplished by analyzing preference similarities between the particularprospective viewer and other viewers. This analysis determines whichother viewers have similarities most closely aligned with theperspective viewer, and when these other viewers have viewed theparticular video clip, it can be inferred from a preference analysisaccording to the invention that the particular perspective viewer has acertain likelihood of choosing to watch the video clip when presented.The stronger the association of preferences between the particularprospective viewer and the other viewers, the higher the likelihood thatthe particular prospective viewer chooses to watch the video clip ifother viewers with similar preferences watched the video clip in apositive manner. Watching the video in a positive manner may include,for example and without limitation, watching the entire clip, sharingthe clip, or purchasing an item or service after watching the clip, tomention a few. A more extensive list of viewership parameters thatindicate a positive viewing experience are described herein. While theconventional method for determining user preferences requires a viewerto rate a video clip actively, an effective alternative or supplementalprocess involves capturing viewership information by passive means andsubsequently determining and analyzing user preferences based oncaptured viewership information.

Monitoring viewer behavior by passive means is useful because users arenot required to spend time rating a video clip, something which they areless likely to do. As such, many more viewers have their viewingbehavior captured, and a larger amount of behavior information iscollected, recorded, and processed than would be otherwise possible ifonly actively supplied information is used for this purpose, assuggested by the prior art. Also, by concentrating on rating the userrather than just the video clip, it is more likely to be able to matchpreference similarities with other viewers because more facets ofviewing behavior are available for analysis.

As such, in addition to allowing users to rate videos actively, anembodiment of the invention passively monitors and records various userbehaviors when a user/viewer interacts with a network video player, e.g.a web video player, while watching an online video clip. In oneembodiment, a data collection agent (DCA) is loaded to the player and/orto a web page that displays the video clip. The DCA passively collectsdetailed viewing and behavior information without requiring any specificinput or actions on the part of the user. Indications of userpreferences are inferred by user actions leading up to viewing thevideo, while viewing the video, and just after and still related toviewing the video. The DCA periodically sends this information to acentral server where it is stored in a central database and where it isused to determine preference similarities among different users.Recorded user preference information may also be used to rate a videoitself.

In one embodiment, the invention comprises a method and/or an apparatusfor monitoring and recording when a user interacts with a video playerwhile watching a video clip online. Specifically, a data collectionagent (DCA) is loaded to the player or to a web page that displays thevideo clip. The DCA collects detailed viewing information andperiodically sends this information to a central server. The viewinginformation is processed by the central server and the central serverthen generates any of a viewership attention span report, a viewershipdemographics report, a viewership geographic report, and a viewershipsharing report. The attention span report and the sharing report provideinferred user preference information that is used to rate a video clippassively without requiring any specific input from the user/viewer. Forpurposes of the discussion herein, the terms watching, viewing, andplaying are used as interchangeably. When a video clip is playing it isassumed that the viewer is viewing or watching the clip. However, theviewer may in fact be watching something else and not the video clip ata particular moment in time. There is no way to know for sure and, thus,the assumption is made.

FIG. 1 is a block schematic diagram of an arrangement of elements 100that may be used in connection with the invention. These elements 100include at least a web server 110 for hosting video sharing websites.The websites include, but are not limited to, YouTube™, MetaCafe™,Yahoo® video, Myspace.com®, users' blogs, etc.

A viewership analytics server (VAS) 120 is configured to connect to eachweb server 110 through a network 130, for example, but not limited to, awide area network (WAN), which enables connectivity such as Internetconnectivity. The VAS 120 executes the tasks related to gathering ofviewership information for web servers 110, analyzing the gatheredinformation, and generating reports on the quantity and characteristicsof viewers, as well as providing information about the duration andrepetition of each view. These tasks are described in greater detailbelow. The VAS 120 is connected to a database 140 in which the collectedand generated viewership data is saved.

Clients 150-1 through 150-M communicate with web servers 110 through thenetwork 130. A client 150 comprises at least a web browser, such asMicrosoft® Internet Explorer, that allows the user to view and navigatethrough web pages downloaded from one or ⋅ more servers 110. Each client150 is capable of downloading, playing, and displaying video clipsprovided by the servers 110. With this aim, each client 150 is capableof running a video player (not shown), which is typically integratedinto a web page. The video player may be, but is not limited to, aFlash-based web player, DivX web player, HTML5 player, Microsoft MediaPlayer, etc.

In accordance with the principles of the invention, a data collectoragent (DCA) is loaded to video sharing websites that are hosted onservers 110 to capture information about the interactions of the viewerswith web players. The DCA may be a script code, e.g. JavaScript, hostedby the VAS 120 and loaded to web pages hosted on servers 110. The DCAmay be also in a form of a plug-in installed in the video playersprovided by video content providers.

The DCA collects and sends metadata and detailed viewing information tothe VAS 120. The metadata comprises at least a video identification(ID), a publisher ID, a website ID that is derived from the uniformresource locator (URL), a length of the video clip being viewed, and thecurrent time. The detailed viewing information includes the actionsperformed on the player and a timestamp. The recorded actions may be,for example, playing, pausing, rewinding, forwarding, and so on. Thetimestamp start and end times are expressed, for example, in secondsfrom the beginning of the video clip. For instance, the pair <play,20-35> means that a user viewed the clip for only for 15 secondsstarting at the 20.sup.th second from the beginning. The pair <pause,30> means that the user paused 30 seconds after the beginning of theclip. The data gathered by the DCA is used by the VAS 120. In oneembodiment, these requests are sent to the VAS 120 in the form of ahypertext transfer protocol (HTTP) request. An HTTP request thatincludes the metadata is sent to the VAS 120 once a web page, includingthe DCA, has completely uploaded to a client's 150 browser. The detailedviewing information, including the pairs of actions and timestamps, isperiodically sent to the VAS 120. The VAS 120 extracts the dataencapsulated in the received requests and saves the data in the database140.

In accordance with one embodiment of the invention users, e.g.advertisers and content publishers, can access the VAS 120 through, forexample, a client 150. This process is similar to that used when a userviewing the content accesses the VAS 120. Advertisers and contentpublishers can designate which websites, publishers, and video clips totrace. In one embodiment of the invention, the user views generated datafrom the VAS 120 by logging onto a website.

FIG. 2 is a block diagram showing the VAS 120 implemented in accordancewith one embodiment of the invention. The VAS 120 includes aninformation collection module 210, an analyzer 220, and a graphical userinterface (GUI) module 230. The collection module 210 communicates witha DCA on a client 150 for the purpose of receiving HTTP requests andresponding thereto.

Specifically, the module 210 generates HTTP responses containing thescript code of the DCA. The information collection module 210 furtherreceives the HTTP requests, including the data collected by the DCA,extracts the information from the requests, and saves the information inthe database 140. This information includes detailed viewing informationand content metadata, which is saved together with tracking dataincluding, but not limited to, the Internet protocol (IP) address, aswell as the operating system and browser type of the client 150. Thedetailed viewing information is saved in an entry associated with thevideo ID. In an exemplary embodiment, the database 140 includes a tablehaving the following fields: video_ID, website_ID, publisher_ID, date,IP, OS, browser type, and <action, timestamp> pairs.

The analyzer 220 processes the information saved in the database 140 togenerate viewership-related analytics data, an attention span report,and viewership demographics. Viewership-related analytics data includes,but is not limited to, the number of viewers during any period of time,e.g. last three days, last week, last months, etc. for a video clip, fora publisher, or for a group of video clips over different periods oftime. This information can be generated for a single website or across aplurality of websites. To generate the analytics data mentioned herein,the analyzer 220 first computes the number of viewers in each day, orany other time interval, from the gathered information. The process forgenerating the viewership-related analytics data is further discussed inU.S. patent application Ser. No. 11/871,880, A Method and System forMonitoring Online Video, assigned to a common assignee, the entirety ofwhich is incorporated herein by this reference thereto.

The analyzer 220 also generates an attention span report that includesdetailed information about the duration and repetition of each view.This report includes, per each video clip, the total number of viewers,and the number of viewers that viewed the complete video clip. Thisreport is produced by processing the data stored in the database 140. Inaddition, the analyzer 220 produces a viewership-geographic report. Thisreport includes the number of viewers of a video clip in each countryaround the globe. The report is generated by correlating the number ofviews with IP addresses of the different viewers. Furthermore, aviewership demographics report is generated by analyzer 220. This reportcorrelates the number of viewers with demographics including race, age,income, educational attainment, employment status, etc. The demographicsare retrieved from the users profiles as saved in the online videowebsites, if and when available.

In accordance with another embodiment, the analyzer 220 can detect fraudattempts. Such attempts are typically performed by browser refreshes orscripting intended to inflate the view count artificially. With thisaim, the analyzer 220 maintains a history file of the video IDs thathave been viewed in the past during a predefined period of time, e.g.video clips viewed in last two hours, by each IP address. If theanalyzer 220 detects multiple views above a threshold from the same IPaddress within a predefined period time, the analyzer 220 discards thedata regarding the subsequent views or any views. The analyzer 220 alsovalidates that the database 140 does not contain timestamp entries withduration longer than the length of the video clip. This check protectsagainst scripting attacks intended to record repeated video views undera single view count.

The GUI 230 displays the viewership-related analytics data produced bythe analyzer 220 as charts or text-based reports. In one embodiment, thecharts are dynamic. That is, the GUI 230 dynamically changes thedisplayed content of the chart as the user changes the chart's timescale. FIGS. 3A-3C show examples of charts of the various reports asgenerated by the GUI 230 according to several embodiments of theinvention. FIG. 3A is a chart that shows an attention span report. FIG.3B is a chart representing the viewership by geography. FIG. 3C showscharts of viewership demographics, specifically, the age distributionand gender distribution of viewers.

FIG. 4 is a flowchart 400 that shows the steps for operating the DCA inaccordance with one embodiment of the invention. When a web page thatincludes a video clip is loaded, the DCA is inserted S410 on the pageand sets a third party cookie in the browser. The third party cookie isused to track the video viewing activity of each unique user across allvideo providers. The DCA is inserted on the web page using an HTTPresponse from the server 110 and contains a script code. The DCAgenerates S420 an HTTP request that includes metadata and sends therequest to the VAS 120. This request contains the provider site in theURL path and the ID of the video being viewed, the local client'scurrent time, the client time zone offset, and the non-personallyidentifiable provider user ID. The VAS 120, upon receiving this request,extracts the metadata and saves it in database 140. Once the video clipis internally loaded in the player, the DCA generates S430 HT″TPrequests that include the detailed viewing information, for example inthe format described above. Thereafter, these HTTP requests areperiodically sent S440 to the VAS 120. Once the web page is closed orthe browser window location is changed while a video clip is beingviewed, the DCA transmits an HTTP request that includes the final datapair that ends at the current viewing time-point.

The earlier discussion regarding FIG. 1 refers to collecting detailedinformation about the duration and repetition for viewing a particularvideo clip. The DCA functionality residing in the viewer's computer 150or supplying website 110 collects this information passively and mayinclude detailed information to enable further analysis of parametersrelated, for instance, to viewing duration and repetition. For instance,the DCA may passively record whether a viewer watched a particular videoclip at all. It may also record what specific portions of a video clipthe viewer watched and the sequence in which they watched thoseportions. Viewers are known to rewind entire videos, rewind portions ofa video, or skip around within a particular video, watching differentportions in a regular or irregular sequence. In fact, many other actionsof a viewer may be passively observed and used to rate the vieweraccording to their preferences as well as rate a video clip itself. FIG.5 is a flowchart 500 that shows the steps for passively collecting andprocessing behavior or viewership information related to a video clip.Viewer actions that reflect their preferences with respect to aparticular video clip may occur before they play the clip, given someforeknowledge of the clip content; while they watch the clip; and afterthey watch the clip. In step S510, parameters respective of a viewer'sbehavior and actions leading up to their playing a video clip arerecorded. Examples of such actions include but are not limited to:

(i) Enduring a pre-roll advertisement, thus showing an affinity and/ortolerance for ads;

(ii) Accessing the video clip by a particular method:

(a) Authenticating or logging-in, thus showing higher interest, to viewthe video clip;

(b) Being incentivized to see the video, such as obtaining points in anonline game; and

(iii) Adjusting the bit rate for streaming or downloading the videoprior to playing it, and in which direction they adjusted the bit rate,e.g. faster or slower.

(iv) Clicking Play in the event that the video isn't auto-playing.

In step S520, parameters respective of a viewer's behavior and actionsduring their playing of a video clip are recorded. Examples of suchactions include but are not limited to:

(i) Adjusting the volume level, muting, and un-muting;

(ii) Pausing and un-pausing;

(iii) Fast-forwarding and rewinding;

(iv) Replaying the video clip, how many times it was replayed, and whatportion was replayed;

(v) Viewing duration and % completion;

(vi) Viewing in full-screen mode;

(vii) Adjusting the bit rate for streaming or downloading the videowhile playing it, and in which direction did they adjust the bit rate,e.g. faster or slower;

(viii) Clicking-through on an overlay image or the video itself to betaken to another webpage or website;

(ix) Spending time viewing certain segments of the video clip that arejudged to have higher value based on actions of previous viewers; and

(x) Enduring a mid-roll advertisement, thus showing an affinity and/ortolerance for ads.

In step S530, parameters respective of a viewer's behavior and actionsafter playing of a video clip are recorded. Examples of such actionsinclude but are not limited to:

(i) Sharing the video via an embed code. Generally, with online video auser can copy a small HTML tag out and paste it into their blog to sharethe video. The system according to the invention tracks the action ofcopying out that HTML code. Such action may be the operation of a buttonor simply the act of highlighting the text in a textbox;

(ii) Sharing the video via E-mail;

(iii) Sharing the video via a social network;

(iv) Sharing the video in multiple separate events and how many separatesharing events the viewer initiated to share the video clip;

(v) Waiting for a recorded time duration between viewing the video clipand initiating a sharing event;

(vi) Bookmarking the video clip for later viewing;

(vii) Downloading and saving the video for later viewing; and

(viii) Subscribing to a channel or feed for the video content produceror artist to become better aware of new videos.

In step S540, a viewer preference score is created for the video clipbased on the particular viewer's behavior and the score is associatedwith the user as metadata. In step S550, a video clip score is createdfor the video clip based on the particular viewer's behavior and thescore is associated with the video clip as metadata. When a score iscalculated for either the user's preference or the video clip itself,the different factors above that contribute to the score are optionallyweighted in the score calculation. For instance, the fact that a videoclip is shared may have more value in calculating a preference scorethan the fact that they paused the video clip. The weighting may also bedifferent for calculating the score for a user's preference relative toa particular clip as opposed to the score for the clip itself. Forinstance, the fact that the viewer watched the pre-roll advertisementmay be more valuable for scoring the user than for scoring the video.Another example of weighting occurs where an un-mute or full-screenaction is considered to be a highly-valuable action, whereas simpleviewing duration for a video that plays automatically may not be, as itmay simply be playing in the user's browser without their attention.

In step S560, the viewer preference score and the video clip score arerecorded in a database.

Network 600 in FIG. 6 shows a flow of information within an embodimentof the invention with regard to sharing of information related to videoclips between users. Sharing typically occurs by way of a wide-areanetwork 130 normally comprising the Internet or a portion thereof. Thesharing process is initiated by the terminal belonging to a primaryviewer/user 150 who decides that a particular video clip is interestingenough or entertaining enough to share with a friend. A terminal orcommunication device 640 as shown would typically be used by a friend ora group of friends. Many types of communication devices or terminals areknown in the art to be used for communication within social networks andinclude PCs and smart phones. The process is initiated when the primaryviewer at terminal 150 is supplied a video clip 610 from a webpage 110.This video clip may be supplied automatically without any initiation bythe user, or it may be supplied to the user upon some action the usertakes to view the video clip. When the primary viewer decides they wishto share a video clip they may be offered the ability to share by way ofwebpage 110, or alternately by providing the link to the particularvideo clip to a third-party link sharing website 660. When a sharingrequest 620 is made by viewer 150 to website 110, website 110 can notifya friend at terminal 640 via email or directly if the friend at terminal640 is logged into website 110.

User 150 may also share the link to a video clip through communicationpath 650 and a third-party website 660 where link sharing is supported.Examples of link sharing websites include, but are not limited to, digg,del.icio.us, and reddit. Links to video clips can also be shared in asimilar manner via social networking sites such as Facebook® andtwitter. Sharing behaviors can be monitored by DCA functions located inthe viewer's computer 150, located on a server at the supplying website110, or both. Data gathered by these DCA functions is periodicallytransferred to VAS 120 for logging, analysis, and reporting. The linksharing website may communicate with a friend using terminal 640 viaemail or directly if terminal 640 is logged into website 660. Inaddition to the sharing mechanisms described in FIG. 6, sharing may alsobe accomplished via an embed code as previously described for step S530of FIG. 5.

Passively monitored sharing activity may include at least one or more ofthe following:

i. If a viewer shared a particular video clip;

ii. How many times they shared the video clip;

iii. How many persons they shared the clip with;

iv. Which sharing mechanisms and/or websites they chose to use to sharethe video clip; and

v. The time duration between when they watched the clip and when theyshared it.

While immediately sharing a video clip certainly shows interest, sharingthe video clip after a period of time has passed shows that the videoclip left a lasting impression on the viewer. Also, should the userinitiate a large number of separate sharing events or sharing actionsfor a particular video clip, this may provide an indication that thevideo clip deserves a higher score for the sharing-based component ofits score.

Thus, passively observed user actions and behaviors are used to rate aparticular user's preferences relative to the video clip in question, aswell as rate the video clip itself.

One parameter that may be passively acquired and then used as acomponent of a viewing preference analysis is viewing duration. FIG. 7shows an exemplary and non-limiting table 700 containing elements of aviewing duration analysis for a sample of video clips. Each of videoclips A through G have been previously viewed by at least two of thethree viewers shown: Joe, Bill, and Jane. For this example, it isassumed for the sake of simplicity that a judgment is made based solelyon viewing duration analysis. In reality, the judgment relative to thelikelihood of a viewer choosing to view a new video is far more complexand may include any or all of the viewership parameters discussedpreviously that may be passively captured prior, during, or after theviewing of a video clip, or alternately proactively supplied by aviewer.

For the analysis of FIG. 7, note that for video A the viewing durationsfor Joe and Bill were both high, indicating a similar preference levelwith regard to this parameter. Likewise for video B, the viewingduration for all three viewers was low, indicating again a similarpreference level. Note that preference similarities may be positive ornegative, and the degree of similarity is used by analysis algorithmsaccording to the invention to determine a likeliness score, that scoreindicating the likelihood that a prospective viewer chooses to watch avideo they have not seen before. For videos C, F, and G, there does notexist enough commonality of viewing to be useful in making a judgment.Thus, the greatest similarity of preferences among the viewers shown inFIG. 7 with regard to previously viewed videos is between Joe and Bill.Later, an opportunity arises to present to Bill a new video 710 calledvideo Z. Bill has not seen video Z before.

An analysis of the database containing preferences relative topreviously viewed videos shows that because Joe had viewed video Z witha 95% viewing duration, there is a high likelihood that Bill chooses towatch video Z when it is offered to him. If video Z is an advertisementvideo clip where an advertiser bids for the opportunity to present theclip, the likeliness score for Bill to watch video Z may be used todetermine the amount of the bid, i.e. the higher the likeliness score,the higher the bid.

FIG. 8 is a block diagram 800 describing one possible organization ofviewers in a database according to the invention. For simplicity and inkeeping with the simplified example of FIG. 7, diagram 800 of FIG. 8refers to an organization in keeping with table 700 of FIG. 7 and, inparticular, refers to the grouping of viewers in the database withrespect to video clip Z. Group (1) 810 comprises viewers of previousvideo clips who have not viewed video clip Z. With respect to FIG. 7,this group contains Bill and Jane. Group (2) 820 comprises viewers whohave viewed clip Z in a positive manner. With respect to FIG. 7, thisgroup contains Joe. Group (3) 830 comprises viewers who have not yetviewed video clip Z, however have preference similarities most closelyaligned to viewers in Group (2) 820 who have previously viewed clip Z ina positive manner. With respect to FIG. 7, Group (3) 830 contains Bill.Thus, Bill is deemed to have a high likelihood of choosing to watchvideo clip Z if and when it is presented to him.

FIG. 9 shows a flowchart 900 whereby preference similarities aredetermined and analyzed to determine the likelihood that a viewerchooses to watch a particular video clip when presented.

In step 910, a database is constructed based on captured viewershipinformation respective of a first set of video clips. The capturingprocess for viewership information may be active whereby the viewerproactively supplies preference information, or passive wherebyviewership information is captured without requiring any action by theviewer in accordance with one or more methods previously described forthe invention.

In step 920, the database is analyzed to determine preferencesimilarities among viewers who have previously viewed the first set ofvideo clips.

In step 930, viewer characteristics respective of a second video clipare captured and recorded in the database. Typically, some of theviewers of the second video clip have previously watched the first videoclip.

Subsequently in step 940, viewership characteristics respective of thesecond video clip are analyzed with respect to the database to produceda list of viewers in the database who are deemed most likely to watchthe second video if and when it is offered to them. As described forFIG. 7, a prospective viewer who has not seen the second video, but hasstrong preference similarities with someone who has responded in apositive manner to viewing the second video, is determined to be alikely candidate to watch the second video when presented. The degree oflikelihood is computed according to the present invention by analyzingeach preference-related parameter of the viewership information,weighting the parameters as appropriate, and developing a likelihoodscore as a result. Should the second video clip be an advertisement, andshould the advertiser be required to place a bid to offer the video tothe prospective viewer, the amount of the bid is determined based atleast in part on the likelihood score.

In step 950, the second video clip is presented to the prospectiveviewer who was previously determined to be part of a list or group ofviewers likely to choose to watch the second video clip.

It should be appreciated by a person skilled in the art that methods,processes and systems described herein can be implemented in software,hardware, firmware, or any combination thereof. The implementation mayinclude the use of a computer system having a processor and a memoryunder the control of the processor, the memory storing instructionsadapted to enable the processor to carry out operations as describedhereinabove. The implementation may be realized, in a concrete manner,as a computer program product that includes a tangible computer readablemedium holding instructions adapted to enable a computer system toperform the operations as described above.

Although the invention is described herein with reference to thepreferred embodiment, one skilled in the art will readily appreciatethat other applications may be substituted for those set forth hereinwithout departing from the spirit and scope of the present invention.Accordingly, the invention should only be limited by the Claims includedbelow.

What is claimed is:
 1. A computer-implemented method for serving videoclips comprising: determining a subset of viewers of a video, fromviewers of the video, that shared the video within a threshold period oftime after viewing the video based on passive actions of the viewers ofthe video; generating a viewer characteristic profile for the videobased on characteristics of viewers in the subset of viewers of thevideo, the viewer characteristic profile for the video reflectingcharacteristics of a viewer most likely to have a positive interactionwith the video; determining a likelihood score of a prospective viewerbased on a profile for the prospective viewer and the viewercharacteristic profile for the video; determining that the likelihoodscore of the prospective viewer is above a defined likelihood level; andserving, over a network, the video to a client device associated withthe prospective viewer based on the determination that the likelihoodscore of the prospective viewer is above the defined likelihood level.2. The computer-implemented method of claim 1, further comprisinginserting a data collection script into a player or webpage thatdisplays videos, the data collection script programmed to monitor vieweractivity relative to the videos.
 3. The computer-implemented method ofclaim 2, further comprising: determining the passive actions of theviewers of the video relative to the video by: receiving HTTP requestsfrom client devices having interacted with the video; and extractingmetadata from the HTTP requests indicating one or more passive actionsrelative to the video; and determining the subset of viewers of thevideo based on the passive actions of the viewers of the video.
 4. Thecomputer-implemented method of claim 1, further comprising: determiningthe passive actions of the viewers of the video relative to the video bydetermining whether the viewers performed, prior to commencement of thevideo, one or more of: viewing a pre-roll advertisement, accessing thevideo by a particular method, adjusting a bit rate for streaming ordownloading the video prior to playing the video, or clicking play ifthe video is not an auto-playing video; and determining the subset ofviewers of the video based on the passive actions of the viewers of thevideo.
 5. The computer-implemented method of claim 1, furthercomprising: determining the passive actions of the viewers of the videorelative to the video by determining whether the viewers performed,during play of the video, one or more of: adjusting a volume level,muting, or un-muting the video, pausing or un-pausing the video,fast-forwarding or rewinding the video, replaying the video, viewing thevideo for a recorded duration or percent completion, viewing the videoin full-screen mode, adjusting a bit rate for streaming or downloadingthe video, clicking-through on an overlay image or on the video itself,spending time viewing certain segments of the video, or viewing amid-roll advertisement; and determining the subset of viewers of thevideo based on the passive actions of the viewers of the video.
 6. Thecomputer-implemented method of claim 1, further comprising: determiningthe passive actions of the viewers of the video relative to the video bydetermining whether the viewers performed, after completion of thevideo, one or more of: sharing the video via an embedded code, sharingthe video via e-mail, sharing the video via a social network, sharingthe video in multiple separate events, bookmarking the video for laterviewing, downloading and saving the video for later viewing, orsubscribing to a channel or feed associated with the video; anddetermining the subset of viewers of the video based on the passiveactions of the viewers of the video.
 7. The computer-implemented methodof claim 1, further comprising determining the subset of viewers of thevideo that shared the video or purchased an item or service afterwatching the video based on the passive actions of the viewers of thevideo.
 8. A system for serving video clips comprising: at least oneserver; and at least one non-transitory computer readable storage mediumstoring instructions thereon, that, when executed by the at least oneserver, cause the system to: determine a subset of viewers of a videothat shared the video within a threshold period of time after viewingthe video based on passive actions of the viewers of the video; generatea viewer characteristic profile for the video based on characteristicsof viewers in the subset of viewers of the video, the viewercharacteristic profile for the video reflecting characteristics of aviewer most likely to have a positive interaction with the video;determine a likelihood score of a prospective viewer based on a profilefor the prospective viewer and the viewer characteristic profile for thevideo; determine that the likelihood score of the prospective viewer isabove a defined likelihood level; and serve, over a network, the videoto a client device associated with the prospective viewer based on thedetermination that the likelihood score of the prospective viewer isabove the defined likelihood level.
 9. The system of claim 8, furthercomprising instructions that, when executed by the at least one server,cause the system to: insert a data collection script into a player orwebpage that displays videos, the data collection script programmed tomonitor viewer activity relative to the videos; and determine thepassive actions of the viewers of the video relative to the video by:receiving HTTP requests from client devices having interacted with thevideo; and extracting metadata from the HTTP requests indicating one ormore passive actions relative to the video.
 10. The system of claim 8,further comprising instructions that, when executed by the at least oneserver, cause the system to: determine the passive actions of theviewers of the video relative to the video during one or more of aperiod of time prior to commencement of the video, during play of thevideo, or after completion of the video; and determine the subset ofviewers of the video based on the passive actions of the viewers of thevideo.
 11. The system of claim 8, further comprising instructions that,when executed by the at least one server, cause the system to: determinethe passive actions of the viewers of the video relative to the video bydetermining whether the viewers performed, prior to commencement of thevideo, one or more of: viewing a pre-roll advertisement, accessing thevideo by a particular method, adjusting a bit rate for streaming ordownloading the video prior to playing the video, or clicking play ifthe video is not an auto-playing video; and determine the subset ofviewers of the video based on the passive actions of the viewers of thevideo.
 12. The system of claim 8, further comprising instructions that,when executed by the at least one server, cause the system to: determinethe passive actions of the viewers of the video relative to the video bydetermining whether the viewers performed, during play of the video, oneor more of: adjusting a volume level, muting, or un-muting the video,pausing or un-pausing the video, fast-forwarding or rewinding the video,replaying the video, viewing the video for a recorded duration orpercent completion, viewing the video in full-screen mode, adjusting abit rate for streaming or downloading the video, clicking-through on anoverlay image or on the video itself, spending time viewing certainsegments of the video, or viewing a mid-roll advertisement; anddetermine the subset of viewers of the video based on the passiveactions of the viewers of the video.
 13. The system of claim 8, furthercomprising instructions that, when executed by the at least one server,cause the system to: determine the passive actions of the viewers of thevideo relative to the video by determining whether the viewersperformed, after completion of the video, one or more of: sharing thevideo via an embedded code, sharing the video via e-mail, sharing thevideo via a social network, sharing the video in multiple separateevents, bookmarking the video for later viewing, downloading and savingthe video for later viewing, or subscribing to a channel or feedassociated with the video; and determine the subset of viewers of thevideo based on the passive actions of the viewers of the video.
 14. Thesystem of claim 8, further comprising instructions that, when executedby the at least one server, cause the system to determine the subset ofviewers of the video that shared the video or purchased an item orservice after watching the video based on the passive actions of theviewers of the video.
 15. A non-transitory computer-readable mediumstoring instructions thereon that, when executed by at least oneprocessor, cause a computing device to: determine a subset of viewers ofa video that shared the video within a threshold period of time afterviewing the video based on passive actions of the viewers of the video;generate a viewer characteristic profile for the video based oncharacteristics of viewers in the subset of viewers of the video, theviewer characteristic profile for the video reflecting characteristicsof a viewer most likely to have a positive interaction with the video;determine a likelihood score of a prospective viewer based on a profilefor the prospective viewer and the viewer characteristic profile for thevideo; determine that the likelihood score of the prospective viewer isabove a defined likelihood level; and serve, over a network, the videoto a client device associated with the prospective viewer based on thedetermination that the likelihood score of the prospective viewer isabove the defined likelihood level.
 16. The non-transitorycomputer-readable medium of claim 15, further comprising instructionsthat, when executed by the at least one processor, cause the computingdevice to: insert a data collection script into a player or webpage thatdisplays videos, the data collection script programmed to monitor vieweractivity relative to the videos; and determine the passive actions ofthe viewers of the video relative to the video by: receiving HTTPrequests from client devices having interacted with the video; andextracting metadata from the HTTP requests indicating one or morepassive actions relative to the video.
 17. The non-transitorycomputer-readable medium of claim 15, further comprising instructionsthat, when executed by the at least one processor, cause the computingdevice to: determine the passive actions of the viewers of the videorelative to the video during one or more of a period of time prior tocommencement of the video, during play of the video, or after completionof the video; and determine the subset of viewers of the video based onthe passive actions of the viewers of the video.
 18. The non-transitorycomputer-readable medium of claim 15, further comprising instructionsthat, when executed by the at least one processor, cause the computingdevice to: determine the passive actions of the viewers of the videorelative to the video by determining whether the viewers performed,prior to commencement of the video, one or more of: viewing a pre-rolladvertisement, accessing the video by a particular method, adjusting abit rate for streaming or downloading the video prior to playing thevideo, or clicking play if the video is not an auto-playing video; anddetermine the subset of viewers of the video based on the passiveactions of the viewers of the video.
 19. The non-transitorycomputer-readable medium of claim 15, further comprising instructionsthat, when executed by the at least one processor, cause the computingdevice to: determine the passive actions of the viewers of the videorelative to the video by determining whether the viewers performed,during play of the video, one or more of: adjusting a volume level,muting, or un-muting the video, pausing or un-pausing the video,fast-forwarding or rewinding the video, replaying the video, viewing thevideo for a recorded duration or percent completion, viewing the videoin full-screen mode, adjusting a bit rate for streaming or downloadingthe video, clicking-through on an overlay image or on the video itself,spending time viewing certain segments of the video, or viewing amid-roll advertisement; and determine the subset of viewers of the videobased on the passive actions of the viewers of the video.
 20. Thenon-transitory computer-readable medium of claim 15, further comprisinginstructions that, when executed by the at least one processor, causethe computing device to: determine the passive actions of the viewers ofthe video relative to the video by determining whether the viewersperformed, after completion of the video, one or more of: sharing thevideo via an embedded code, sharing the video via e-mail, sharing thevideo via a social network, sharing the video in multiple separateevents, bookmarking the video for later viewing, downloading and savingthe video for later viewing, or subscribing to a channel or feedassociated with the video; and determine the subset of viewers of thevideo based on the passive actions of the viewers of the video.